Search Results (85)

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team’s mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover’s location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover’s localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations. Full article

Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient’s unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models’ recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target’s likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient’s demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade. Full article

Microsatellites have significantly impacted space missions by offering advanced technology at a low cost. This study introduces an attitude determination and control algorithm for agile whiskbroom scanning maneuvers in microsatellites to enable wide-swath target detection for low-Earth-orbit microsatellites. First, an angular velocity calculation model for agile whiskbroom scanning is established. A methodology has been developed to calculate the maximum available time for whiskbroom scanning from one side of the sub-satellite point to the other while ensuring the seamless joining of adjacent strips to avoid missing targets. Thereafter, a gyro- and magnetometer-based cubature Kalman filter is put forward for microsatellite attitude estimation. Furthermore, for attitude control, a hybrid manipulation law capable of preventing singularities and escaping singularity surfaces is designed to ensure high-precision torque output from the control moment gyroscopes (CMGs) used as actuators. The benefits of the linear sliding mode and fast terminal sliding mode are integrated, and a non-singular sliding surface is designed, yielding a non-singular fast terminal sliding mode attitude control algorithm for tracking the desired trajectory. This algorithm effectively suppresses chattering and enhances dynamic performance without using a switching term. A semi-physical simulation experiment system is also conducted on the ground to validate the proposed algorithm’s high-precision tracking of the planned whiskbroom scanning path. The experimental results demonstrate an attitude angle control accuracy of 4 × 10⁻² degrees and angular velocity control accuracy of 0.01°/s and thus the effectiveness of the proposed algorithm. Full article

In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model then classifies the preprocessed images into distinct tumor types: glioma, meningioma, and pituitary tumor. Following classification, the RUNet model performs pixel-level segmentation to delineate tumor regions within the MRI scans. The FT-CNN leverages the VGG19 architecture, pre-trained on large datasets and fine-tuned for specific tumor classification tasks. Features extracted from MRI images are used to train the FT-CNN, demonstrating robust performance in discriminating between tumor types. Subsequently, the RUNet model, inspired by the U-Net design and enhanced with residual blocks, effectively segments tumors by combining high-resolution spatial information from the encoding path with context-rich features from the bottleneck. My experimental results indicate that the integrated pipeline achieves high accuracy in both classification (96%) and segmentation tasks (98%), showcasing its potential for clinical applications in brain tumor diagnosis. For the classification task, the metrics involved are loss, accuracy, confusion matrix, and classification report, while for the segmentation task, the metrics used are loss, accuracy, Dice coefficient, intersection over union, and Jaccard distance. To further validate the generalizability and robustness of the integrated pipeline, I evaluated the model on two additional datasets. The first dataset consists of 7023 images for classification tasks, expanding to a four-class dataset. The second dataset contains approximately 3929 images for both classification and segmentation tasks, including a binary classification scenario. The model demonstrated robust performance, achieving 95% accuracy on the four-class task and high accuracy (96%) in the binary classification and segmentation tasks, with a Dice coefficient of 95%. Full article

SLAM (simultaneous localization and mapping) is essential for accurate positioning and reasonable path planning in outdoor mobile robots. LiDAR SLAM is currently the dominant method for creating outdoor environment maps. However, the mainstream LiDAR SLAM algorithms have a single point cloud feature extraction process at the front end, and most of the loop closure detection at the back end is based on RNN (radius nearest neighbor). This results in low mapping accuracy and poor real-time performance. To solve this problem, we integrated the functions of point cloud segmentation and Scan Context loop closure detection based on the advanced LiDAR-inertial SLAM algorithm (LIO-SAM). First, we employed range images to extract ground points from raw LiDAR data, followed by the BFS (breadth-first search) algorithm to cluster non-ground points and downsample outliers. Then, we calculated the curvature to extract planar points from ground points and corner points from clustered segmented non-ground points. Finally, we used the Scan Context method for loop closure detection to improve back-end mapping speed and reduce odometry drift. Experimental validation with the KITTI dataset verified the advantages of the proposed method, and combined with Walking, Park, and other datasets comprehensively verified that the proposed method had good accuracy and real-time performance. Full article

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. Full article

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×10⁶) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads. Full article

Automated progress monitoring of construction sites using cameras has been proposed in recent years. Although previous studies have tried to identify the most informative camera views according to 4D BIM to optimize installation plans, video collection using fixed or pan-tilt-zoom cameras is still limited by their inability to adapt to the dynamic construction environment. Therefore, considerable attention has been paid to using camera-equipped unmanned aerial vehicles (CE-UAVs), which provide mobility for the camera, allowing it to fit its field of view automatically to the important parts of the construction site while avoiding occlusions. However, previous studies on optimizing video collection with CE-UAV are limited to the scanning of static objects on construction sites. Given the growing interest in construction activities, the existing methods are inadequate to meet the requirements for the collection of high-quality videos. In this study, the following requirements for and constraints on collecting construction-activity videos have been identified: (1) the FOV should be optimized to cover the areas of interest with the minimum possible occlusion; (2) the path of the UAV should be optimized to allow efficient data collection on multiple construction activities over a large construction site, considering the locations of activities at specific times; and (3) the data collection should consider the requirements for CV processes. Aiming to address these requirements and constraints, a method has been proposed to perform simulation-based optimization of path planning for CE-UAVs to allow automated and effective collection of videos of construction activities based on a detailed 4D simulation that includes a micro-schedule and the corresponding workspaces. This method can identify the most informative views of the workspaces and the optimal path for data capture. A case study was developed to demonstrate the feasibility of the proposed method. Full article

The research on additive manufacturing (AM) path planning mainly focuses on the traditional three-axis AM path planning and five-degree-of-freedom (DOF) AM path planning, while there is less research on six-DOF AM path planning. In the traditional AM path planning algorithm, the filling path is discontinuous and there is long straight-line printing in a certain direction, which can easily lead to warpage deformation. Therefore, in this work, the six-DOF manipulator is taken as the main object to build an AM platform, and the mechanism of AM path planning of the manipulator is studied. The path planning algorithm combining the contour offset filling method and Hilbert curve filling is optimized by using a cubic uniform B-spline curve, and an AM path planning algorithm suitable for a six-DOF manipulator is obtained. A continuous printing path can be generated by this algorithm. It reduces the existence of long straight-line printing in a certain direction, thereby reducing the warpage deformation of the model and improving the molding quality of the model. The traditional three-axis AM device and the six-DOF AM platform were used to print two kinds of models. By comparing the printing time, the six-DOF AM platform was 43.70% and 37.94% shorter than the traditional three-axis AM device. The same model was printed on a six-DOF AM platform by using the parallel scanning filling method, the path planning algorithm combining contour offset and Hilbert curve, and the method proposed in this paper. Through experimental verification, the average warpage deformation of the model printed by the method proposed in this paper was reduced by 37.81% and 13.79%, respectively, compared with the other two methods. Full article

The aim of manufacturing large aerospace parts for the three-dimensional scanning field demands high precision and efficiency. However, it may be more challenging to meet the full coverage of the measurement problems for large aerospace parts with the scanning range of traditional three-dimensional scanning methods. This paper establishes a dynamic posturing scanning measurement system for large aerospace parts with a six-degree-of-freedom posturing platform and a six-degree-of-freedom industrial robot linkage. It establishes a mathematical model of dynamic three-dimensional scanning posturing. It proposes a platform attitude adjustment strategy based on the field of view angle of a 3D scanner during the adjustment of a six-degree-of-freedom platform. The dynamic scanning path planning is carried out using the three-dimensional spatial decomposition method, and the vector coordinates of the critical points at the edges of the missing areas of the scan are used to re-scan the missing areas to establish the dynamic scanning paths of large aerospace parts. It is experimentally verified that the system can realize the dynamic scanning of complex curved large aerospace parts. The experimental results show that the measurement efficiency is improved by more than 75%, and the point cloud coverage of the scanning reconstruction is improved by 18% for large aerospace components with complex surfaces. Full article

Background/Objectives: Osteoporotic vertebral fractures (OVFs) significantly impair quality of life. This study evaluates the impact of STIR sequence MR imaging on clinical decision-making for treating OVFs, mainly focusing on how MRI findings influence treatment modifications compared to those based solely on CT scans. Methods: This retrospective analysis reviewed cases from the Manninger Jenő National Traumatology Institute over ten years, where patients with suspected OVFs underwent CT and STIR sequence MR imaging. The study examined changes in treatment plans initiated by MRI findings. The diagnostic effectiveness of MRI was compared against CT in terms of sensitivity, specificity, and the ability to influence clinical treatment paths. Results: MRI detected 1.65 times more fractures than CT scans. MRI influenced treatment adjustments in 67% of cases, leading to significant changes from conservative–conservative, conservative–surgery, and surgery–surgery based on fracture characterizations provided by MRI. Conclusions: This study demonstrates that integrating STIR sequence MR imaging into the diagnostic pathway for OVFs significantly enhances the accuracy of fracture detection and profoundly impacts treatment decisions. The ability of MRI to reveal specific fracture features that are not detectable by CT scans supports its importance in the clinical evaluation of OVFs, suggesting that MRI should be incorporated more into diagnostic protocols to improve patient management and outcomes. The findings advocate for further research to establish STIR MRI as a standard osteoporosis management tool and explore its long-term benefits in preventing secondary fractures. Full article

Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the irregular shape of the sensor’s projection caused by the step-stare rotational motion. To address this problem, this paper proposes an efficient, seamless CPP method with an adaptive hyperbolic grid. First, we convert the coverage problem in Euclidean space to a tiling problem in spherical space. A spherical approximate tiling method based on a zonal isosceles trapezoid is developed to construct a seamless hyperbolic grid. Then, we present a dual-caliper optimization algorithm to further compress the grid and improve the coverage efficiency. Finally, both boustrophedon and branch-and-bound approaches are utilized to generate rotation paths for different scanning scenarios. Experiments were conducted on a custom dataset consisting of 800 diverse geometric regions (including 2 geometry types and 40 samples for 10 groups). The proposed method demonstrates comparable performance of closed-form path length relative to that of a heuristic optimization method while significantly improving real-time capabilities by a minimum factor of 2464. Furthermore, in comparison to traditional rule-based methods, our approach has been shown to reduce the rotational path length by at least 27.29% and 16.71% in circle and convex polygon groups, respectively, indicating a significant improvement in planning efficiency. Full article

High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection. Full article

When addressing the technical challenges of achieving precise force tracking during the local polishing process of polishing robots, controlling the contact state between the robot and the workpiece surface is essential. To this end, a contact motion-planning strategy based on dynamic systems is designed to generate trajectory routes during local polishing. The trajectory simulation of the local modulation dynamic system is achieved through the employment of the support vector regression (SVR) algorithm with a Gaussian kernel, facilitating the learning process. The feasibility and stability of planning local paths are validated using the local modulation dynamic system. To maintain a constant contact force between the end-effector polishing robot and the workpiece, an integral adaptive impedance control strategy is utilized, enabling the robot’s compliant control. Subsequently, an experimental system for the polishing robot is constructed in order to verify the effectiveness of the motion-planning system. The experimental results demonstrate that the proposed motion-planning approach is applicable in practical polishing processes, ensuring smooth contact and maintaining the desired contact force when scanning nonlinear surfaces, and thus showcasing stability and practicality. Full article

Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the 3D scan of the patient to CAD-supported modelling of the corrective form and the orthosis design until the path planning of a printable geometry. The main disadvantages of current additive printing techniques, such as the fused filament fabrication (FFF) process, are high printing times (>12 h) for larger components as well as the low degree of freedom in the 2.5D printing technique that prevent the subsequent application of geometry features to the product. The fast SEAMHex (Screw Extrusion Additive Manufacturing) printing technology with a hexapod kinematic printing bed provides a solution to the mentioned difficulties. Consequently, the high-performance printer has been prepared for the individual requirements of medical technology in terms of materials and geometries. An effective additive manufacturing process has been realised and tested in combination with a digital process chain for orthosis modelling. Full article